Aula 19, M3
Sala de Situação - UnB
O que é GIS, como usar ggplot para Mapas …
Mapa do Brasil usando shapefiles salvos no computador..
Geographic Information System (GIS)
Vetor: Pontos, linhas e polígonos
Raster: Pixels
Geralmente em Shapefiles
coleção de arquivos: .shp, .shx, and .dbf. ou mais…
Localização na superfície da terra
Sistema de coordenadas
Pacotes, baixam direto os shapefiles
Ler de shapefiles no disco (seu computurador)
Não é um dataframe
Mas não importa, vamos fingir que é
Simple feature collection with 241 features and 63 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -180 ymin: -89.99893 xmax: 180 ymax: 83.59961
Geodetic CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
First 10 features:
scalerank featurecla labelrank sovereignt sov_a3 adm0_dif
0 3 Admin-0 country 5 Netherlands NL1 1
1 1 Admin-0 country 3 Afghanistan AFG 0
2 1 Admin-0 country 3 Angola AGO 0
3 1 Admin-0 country 6 United Kingdom GB1 1
4 1 Admin-0 country 6 Albania ALB 0
5 3 Admin-0 country 6 Finland FI1 1
6 3 Admin-0 country 6 Andorra AND 0
7 1 Admin-0 country 4 United Arab Emirates ARE 0
8 1 Admin-0 country 2 Argentina ARG 0
9 1 Admin-0 country 6 Armenia ARM 0
level type admin adm0_a3 geou_dif
0 2 Country Aruba ABW 0
1 2 Sovereign country Afghanistan AFG 0
2 2 Sovereign country Angola AGO 0
3 2 Dependency Anguilla AIA 0
4 2 Sovereign country Albania ALB 0
5 2 Country Aland ALD 0
6 2 Sovereign country Andorra AND 0
7 2 Sovereign country United Arab Emirates ARE 0
8 2 Sovereign country Argentina ARG 0
9 2 Sovereign country Armenia ARM 0
geounit gu_a3 su_dif subunit su_a3 brk_diff
0 Aruba ABW 0 Aruba ABW 0
1 Afghanistan AFG 0 Afghanistan AFG 0
2 Angola AGO 0 Angola AGO 0
3 Anguilla AIA 0 Anguilla AIA 0
4 Albania ALB 0 Albania ALB 0
5 Aland ALD 0 Aland ALD 0
6 Andorra AND 0 Andorra AND 0
7 United Arab Emirates ARE 0 United Arab Emirates ARE 0
8 Argentina ARG 0 Argentina ARG 0
9 Armenia ARM 0 Armenia ARM 0
name name_long brk_a3 brk_name
0 Aruba Aruba ABW Aruba
1 Afghanistan Afghanistan AFG Afghanistan
2 Angola Angola AGO Angola
3 Anguilla Anguilla AIA Anguilla
4 Albania Albania ALB Albania
5 Aland Aland Islands ALD Aland
6 Andorra Andorra AND Andorra
7 United Arab Emirates United Arab Emirates ARE United Arab Emirates
8 Argentina Argentina ARG Argentina
9 Armenia Armenia ARM Armenia
brk_group abbrev postal formal_en formal_fr note_adm0
0 <NA> Aruba AW Aruba <NA> Neth.
1 <NA> Afg. AF Islamic State of Afghanistan <NA> <NA>
2 <NA> Ang. AO People's Republic of Angola <NA> <NA>
3 <NA> Ang. AI <NA> <NA> U.K.
4 <NA> Alb. AL Republic of Albania <NA> <NA>
5 <NA> Aland AI Åland Islands <NA> Fin.
6 <NA> And. AND Principality of Andorra <NA> <NA>
7 <NA> U.A.E. AE United Arab Emirates <NA> <NA>
8 <NA> Arg. AR Argentine Republic <NA> <NA>
9 <NA> Arm. ARM Republic of Armenia <NA> <NA>
note_brk name_sort name_alt mapcolor7 mapcolor8 mapcolor9
0 <NA> Aruba <NA> 4 2 2
1 <NA> Afghanistan <NA> 5 6 8
2 <NA> Angola <NA> 3 2 6
3 <NA> Anguilla <NA> 6 6 6
4 <NA> Albania <NA> 1 4 1
5 <NA> Aland <NA> 4 1 4
6 <NA> Andorra <NA> 1 4 1
7 <NA> United Arab Emirates <NA> 2 1 3
8 <NA> Argentina <NA> 3 1 3
9 <NA> Armenia <NA> 3 1 2
mapcolor13 pop_est gdp_md_est pop_year lastcensus gdp_year
0 9 103065 2258.0 NA 2010 NA
1 7 28400000 22270.0 NA 1979 NA
2 1 12799293 110300.0 NA 1970 NA
3 3 14436 108.9 NA NA NA
4 6 3639453 21810.0 NA 2001 NA
5 6 27153 1563.0 NA NA NA
6 8 83888 3660.0 NA 1989 NA
7 3 4798491 184300.0 NA 2010 NA
8 13 40913584 573900.0 NA 2010 NA
9 10 2967004 18770.0 NA 2001 NA
economy income_grp wikipedia fips_10 iso_a2
0 6. Developing region 2. High income: nonOECD NA <NA> AW
1 7. Least developed region 5. Low income NA <NA> AF
2 7. Least developed region 3. Upper middle income NA <NA> AO
3 6. Developing region 3. Upper middle income NA <NA> AI
4 6. Developing region 4. Lower middle income NA <NA> AL
5 2. Developed region: nonG7 1. High income: OECD NA <NA> AX
6 2. Developed region: nonG7 2. High income: nonOECD NA <NA> AD
7 6. Developing region 2. High income: nonOECD NA <NA> AE
8 5. Emerging region: G20 3. Upper middle income NA <NA> AR
9 6. Developing region 4. Lower middle income NA <NA> AM
iso_a3 iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un
0 ABW 533 533 AW ABW NA ABW ABW NA
1 AFG 004 004 AF AFG NA AFG AFG NA
2 AGO 024 024 AO AGO NA AGO AGO NA
3 AIA 660 660 <NA> <NA> NA AIA AIA NA
4 ALB 008 008 AL ALB NA ALB ALB NA
5 ALA 248 248 <NA> <NA> NA ALA ALD NA
6 AND 020 020 AD ADO NA AND AND NA
7 ARE 784 784 AE ARE NA ARE ARE NA
8 ARG 032 032 AR ARG NA ARG ARG NA
9 ARM 051 051 AM ARM NA ARM ARM NA
adm0_a3_wb continent region_un subregion region_wb
0 NA North America Americas Caribbean Latin America & Caribbean
1 NA Asia Asia Southern Asia South Asia
2 NA Africa Africa Middle Africa Sub-Saharan Africa
3 NA North America Americas Caribbean Latin America & Caribbean
4 NA Europe Europe Southern Europe Europe & Central Asia
5 NA Europe Europe Northern Europe Europe & Central Asia
6 NA Europe Europe Southern Europe Europe & Central Asia
7 NA Asia Asia Western Asia Middle East & North Africa
8 NA South America Americas South America Latin America & Caribbean
9 NA Asia Asia Western Asia Europe & Central Asia
name_len long_len abbrev_len tiny homepart geometry
0 5 5 5 4 NA MULTIPOLYGON (((-69.89912 1...
1 11 11 4 NA 1 MULTIPOLYGON (((74.89131 37...
2 6 6 4 NA 1 MULTIPOLYGON (((14.19082 -5...
3 8 8 4 NA NA MULTIPOLYGON (((-63.00122 1...
4 7 7 4 NA 1 MULTIPOLYGON (((20.06396 42...
5 5 13 5 5 NA MULTIPOLYGON (((20.61133 60...
6 7 7 4 5 1 MULTIPOLYGON (((1.706055 42...
7 20 20 6 NA 1 MULTIPOLYGON (((53.92783 24...
8 9 9 4 NA 1 MULTIPOLYGON (((-64.54917 -...
9 7 7 4 NA 1 MULTIPOLYGON (((45.55234 40...
# MANIPULAÇÕES NO BANCO -----------
# OMS
who_trat <- who_bruto %>%
dplyr::select(WHO_region,Date_reported,Country,New_cases,Cumulative_cases, New_deaths)%>%
dplyr::group_by(WHO_region, Country) %>% # agrupando (tabela dinamica)
dplyr::summarise(Acumulado=max(Cumulative_cases, na.rm=T))
# Shape
mundo_trat <- mundo %>%
dplyr::rename(Country=admin) # PARA PODER FAZER O JOINVamos dar olhada num papa genérico.
Mundo:
covid_mundo_com_shape_iso2<- covid_mundo_com_shape_iso %>%
mutate(Acumulado_cat=cut(Acumulado,
breaks=c(-1,17000,160000,1000000,
max(covid_mundo_com_shape_iso$Acumulado, na.rm=T)+1),
labels=c("até 17 mil", "17 mil - 160 mil ",
"160 mil - 1 milhão", "acima de 1 milhão")))
paleta<-c('#feebe2','#fbb4b9','#f768a1','#ae017e')
ggplot(covid_mundo_com_shape_iso2, fill="white")+
geom_sf(aes(geometry=geometry, fill=Acumulado_cat ),
color="purple", # cor da fronteira
lwd=0.1) + # finura da fronteira
scale_fill_manual(values=paleta, name="Casos Acumulados \n de covid-19 no Mundo")+
theme_map()+
theme(panel.background = element_rect(fill = "lightblue"),
legend.position = "bottom")+
ggtitle("Um mapa bonito")#oceano
ggsave("mapa_rosa.png")brasil_bruto <- import("Exercicios/covid_br_2022.csv")
estados <- read_sf(dsn = "Exercicios/shapefiles/.",
layer="UFBR")
# tratando os dados
brasil_acumulado_estado <- brasil_bruto %>%
filter(municipio!="")%>%
group_by(estado) %>%
summarise(Acumulado=sum(casosNovos, na.rm=T))
estados_trat <- estados %>%
rename(estado=SIGLA) %>%
st_zm() # as vezes precisa, nem sempre
covid_brasil_com_shape <- left_join(brasil_acumulado_estado, estados_trat) function geography
1 `read_country` Country
2 `read_region` Region
3 `read_state` States
4 `read_meso_region` Meso region
5 `read_micro_region` Micro region
6 `read_intermediate_region` Intermediate region
years
1 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
2 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
3 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
4 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
5 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
6 2017, 2019, 2020
source
1 IBGE
2 IBGE
3 IBGE
4 IBGE
5 IBGE
6 IBGE
regiao_geobr <- read_region() %>%
rename(regiao=`name_region`) %>%
mutate(regiao=if_else(regiao=="Centro Oeste", "Centro-Oeste", regiao))
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funcao_limpeza<-function(x){
limpeza_acentos <- list('Š'='S', 'š'='s', 'Ž'='Z', 'ž'='z', 'À'='A', 'Á'='A', 'Â'='A', 'Ã'='A', 'Ä'='A', 'Å'='A', 'Æ'='A', 'Ç'='C', 'È'='E', 'É'='E','Ê'='E', 'Ë'='E', 'Ì'='I', 'Í'='I', 'Î'='I', 'Ï'='I', 'Ñ'='N', 'Ò'='O', 'Ó'='O', 'Ô'='O', 'Õ'='O', 'Ö'='O', 'Ø'='O', 'Ù'='U','Ú'='U', 'Û'='U', 'Ü'='U', 'Ý'='Y', 'Þ'='B', 'ß'='Ss' )
gsubfn(paste(names(limpeza_acentos),collapse='|'), limpeza_acentos,x)}
ras_df <- read_sf( dsn = "Exercicios/shapefiles/.",
layer="Regioes_Administrativas")%>%
mutate(RA=funcao_limpeza(ra))